Jove
Visualize
Contact Us

Related Concept Videos

Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

523
Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
523
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

726
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
726
Pilot and Numeric Relaying01:21

Pilot and Numeric Relaying

480
Pilot relaying is a type of differential protection used in power systems. It compares electrical quantities at the terminals of equipment via a communication channel instead of direct relay interconnection. This method is essential for transmission lines where the terminals are far apart, typically up to 80 km for lines with 69 to 115 kV ratings. Four types of communication channels are used for pilot relaying:
480
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

497
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
497
Power System Distribution01:25

Power System Distribution

1.0K
Power system distribution involves delivering electrical energy from power plants to consumers through a network of transmission and distribution systems. The process begins at power plants, where energy from coal, gas, nuclear, water, and wind is converted into electrical energy. These plants use three-phase generators, typically rated between 50 to 1300 MVA, with terminal voltages ranging from a few kV to 20 kV, depending on the size and age of the units.
The transmission system is designed...
1.0K
Zones of Protection01:16

Zones of Protection

757
In power systems, the entire setup is divided into protective zones to isolate faults and protect the rest of the network. These zones include generators, transformers, buses, transmission lines, distribution lines, and motors. Each zone can be visualized as a separate room in a house, with each room protected by its own circuit breaker.
Protective zones are defined by closed dashed lines, containing one or more components. A key characteristic of these zones is the strategic placement of...
757

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Correction: Performance-driven switched reluctance motor drive using multiport cascaded converter and advanced direct torque control scheme.

Scientific reports·2026
Same author

An integrated machine learning framework for EV charging management.

Scientific reports·2026
Same author

A novel multi-stage attack dataset for smart home intrusion detection.

Data in brief·2026
Same author

Performance-driven switched reluctance motor drive using multiport cascaded converter and advanced direct torque control scheme.

Scientific reports·2026
Same author

Retraction notice to "Enhancing data security and privacy in energy applications: Integrating IoT and blockchain technologies" [Heliyon 10 (2024) e38917].

Heliyon·2026
Same author

Bayat-driven FOPID controller design for biogas-based microgrid with real-time validation.

Scientific reports·2025
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jan 15, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K

AI-driven cybersecurity framework for anomaly detection in power systems.

Vignes V M1, Sri Harini M P1, Rahul Satheesh2

  • 1Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India.

Scientific Reports
|October 10, 2025
PubMed
Summary
This summary is machine-generated.

This study presents an AI cybersecurity framework for smart grids, enhancing anomaly detection by fusing cyber and physical data. It achieves high accuracy and resilience against cyber threats, ensuring reliable power system operation.

Keywords:
Adversal analysisCybersecurityData fusionIntrusion detection systemLSTMRandom forestReal-world implementationSHAPSmart grid

Related Experiment Videos

Last Updated: Jan 15, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Power Systems Engineering

Background:

  • Smart grids face increasing cyber threats like False Data Injection Attacks (FDIA), Denial-of-Service (DoS), and Man-in-the-Middle (MiTM) attacks.
  • Traditional security methods are insufficient due to a lack of contextual awareness and real-time adaptability.
  • The integration of IoT and automation in smart grids escalates the sophistication and frequency of these threats.

Purpose of the Study:

  • To introduce an AI-driven cybersecurity framework for high-accuracy anomaly detection in power systems.
  • To fuse cyber and physical datasets for enhanced threat identification.
  • To improve the resilience and explainability of smart grid security.

Main Methods:

  • Utilized Long Short-Term Memory (LSTM) networks and Random Forest classifiers for anomaly detection.
  • Employed SHapley Additive exPlanations (SHAP) for model interpretability.
  • Implemented Fast Gradient Sign Method (FGSM)-based adversarial training to enhance robustness against adversarial attacks.

Main Results:

  • Achieved 99.798% accuracy in binary classification and 98.1919% accuracy in multi-class classification.
  • Improved adversarial accuracy from 95.15% to 99.39% through adversarial training.
  • Demonstrated practical viability with model inference completed in 2.16 seconds on a Xilinx PYNQ-Z2 edge device.

Conclusions:

  • The proposed AI framework offers high efficacy, explainability, and operational resilience for smart grid cybersecurity.
  • The fusion of cyber and physical data significantly enhances anomaly detection capabilities.
  • The framework is well-suited for real-time deployment in smart grid environments to ensure secure and reliable power system operation.